# AI Maturity Model for Nonprofits pt 2

An episode of [Community IT Innovators Nonprofit Technology Topics](https://invisiblenetwork.ai/podcast/1536843269), 24 Jul 2026, 29 min.

Audio: https://www.buzzsprout.com/1029925/episodes/19533451-ai-maturity-model-for-nonprofits-pt-2.mp3

In Part 2 of the AI Maturity Model for Nonprofits webinar, Carolyn Woodard and Mimi Yeh, Engagement Director at PTKO, and George Danilovics, VP of Technology at AHIP, pick up where Part 1 left off, moving from the framework itself to the harder question of what it takes to make AI adoption become quiet, boring, and everywhere. Mimi and George walk through what it looks like when AI stops being an initiative and becomes infrastructure, pointing to the moment when people stop announcing they used it, the way no one says "I used Outlook to send this email." The session closes with a candid Q&A on tool volatility, vendor risk, and how to address genuine ethical concerns about AI when your colleagues or community have serious reservations. Haven't listened to Part 1 yet? Find it in your podcast feed. This episode covers: • Why you should map each team separately against the five maturity stages rather than assigning your organization a single score, and how that map becomes a diagnostic you can actually act on. • Why AI competency training deserves the same recurring, role-based architecture as security awareness training, not a one-time brown bag. • Action items you can start this week and this quarter to move one team forward one stage at a time. • How to think about vendor risk and tool volatility when planning for long-term AI integration, and why documenting your strategy matters more than committing to any one tool, and lets you move between vendors more easily down the road. • Practical guidance on addressing ethical and social concerns about AI, including when those conversations belong at the board level. Resources Mentioned: • AI Maturity Model for Nonprofits Webinar – Community IT Innovators – https://communityit.com/webinar-ai-maturity-model-for-nonprofits/?utm_source=podcast&utm_medium=referral&utm_campaign=friday-tech-topics • AI Journey Guide and Supplemental Resources – PTKO – https://ptko.io/ai-toolkit/ • Using AI to Build a Data Lake for Nonprofits Webinar (August 19) – Community IT Innovators – https://communityit.com/webinar-ai-in-practice-for-nonprofits/?utm_source=podcast&utm_medium=referral&utm_campaign=friday-tech-topics • Nonprofit IT Management Community – Reddit – https://www.reddit.com/r/nonprofitITmanagement _______________________________ Start a conversation :) • Register to attend a webinar in real time, and find all past transcripts at https://communityit.com/webinars/ • email Carolyn at cwoodard@communityit.com • on LinkedIn • on reddit/r/nonprofitITmanagement • on the Community IT website Thanks for listening.

## Transcript

[0:23] Community IT Intro: Thank you for joining this Community IT podcast part two.
[0:27] Community IT Intro: You can find part one in your podcast feed if you subscribe wherever you listen to podcasts.
[0:35] Carolyn Woodard: Welcome everyone to the Community IT Innovators webinar.
[0:38] Carolyn Woodard: This is the AI Maturity Model for Nonprofits how AI Becomes Quiet, Boring, and everywhere.
[0:45] Mimi Yeh: I'm Mimi Yeh and I'm an engagement director at PTKO.
[0:49] George Danilovics: Hello, everyone.
[0:50] George Danilovics: My name is George Danilovics.
[0:52] George Danilovics: Thank you all for joining this afternoon.
[0:54] George Danilovics: I am the VP of technology at AHIP.
[0:57] Mimi Yeh: So let's do a short reframe in order to remove some anxiety.
[1:01] Mimi Yeh: Again, not only is the goal not for everybody to reach that pioneering stage.
[1:09] Mimi Yeh: I think it's also important to consider that when people look at models like this, the first question that comes to mind is where is my organization?
[1:19] Mimi Yeh: Where do I sit on this maturity model?
[1:22] Mimi Yeh: And that might not be the right or the best question to ask.
[1:28] Mimi Yeh: Something that we really advise leaders to think about when it comes to AI is to try not to give your organization a single maturity score because your organization is not going to use AI in the same way in all of your different departments and teams.
[1:45] Mimi Yeh: Your finance team might be way ahead of your HR team.
[1:51] Mimi Yeh: Marketing might be experimenting, programs might not have started yet.
[1:56] Mimi Yeh: And that is totally normal.
[1:59] Mimi Yeh: This is an uneven picture of the organization's progress, but it is not a reflection of failure.
[2:06] Mimi Yeh: And it's not a reflection of poor planning.
[2:09] Mimi Yeh: That unevenness is actually quite useful.
[2:12] Mimi Yeh: When you map each team separately, you get a diagnostic rather than just a grade.
[2:19] Mimi Yeh: And that diagnostic helps you see exactly where your internal success stories are for AI and which teams might be able to mentor others in their AI journey based off of their experiences and based off of the lessons that they've learned.
[2:37] Mimi Yeh: Your fastest moving teams are the best trainers and advocates.
[2:42] Mimi Yeh: They are the ones who know AI and they know AI in the context of what your organization does and the constraints and opportunities facing your organization.
[2:56] Mimi Yeh: So if you were to do a self-evaluation exercise against this maturity model, we would say resist the urge to produce one number or one stage for the whole organization and instead produce a map, because that map is something that you can act on with real next steps.
[3:17] Mimi Yeh: I think it is time for another poll.
[3:20] Carolyn Woodard: It's time for our next poll.
[3:21] Carolyn Woodard: So talking about maps and where you are.
[3:23] Carolyn Woodard: So this one is actually multiple choice.
[3:26] Carolyn Woodard: So you can go ahead and choose as many as apply to you.
[3:32] Carolyn Woodard: So the question is which teams at your organization are using AI on a regular basis?
[3:39] Carolyn Woodard: And I'm gonna be so interested to see this because I have in mind what I think it's gonna be, and I want to see what happens.
[3:45] Carolyn Woodard: So the teams that you uh can identify as using AI on a regular basis are membership, marketing, fundraising, development, um, finance, IT, customer service, programming, something else.
[4:04] Carolyn Woodard: So if you have another team that is using AI, please put that in the chat.
[4:09] Carolyn Woodard: And uh not applicable again.
[4:12] Carolyn Woodard: And I just, you know, given that we were just uh we didn't talk about it, but in chat, there were some questions about if you're a health-oriented nonprofit, and so you have like additional compliance rules.
[4:25] Carolyn Woodard: Um, and then how can you evaluate if the AI is going to still be in compliance with the rules that govern you or with your own policy?
[4:33] Carolyn Woodard: And uh, it's a thorny question.
[4:35] Carolyn Woodard: So we were talking a little bit about it in chat, but I think that kind of reflects on this as well.
[4:40] Carolyn Woodard: So you, as you were saying, Mimi, you might have a team that is super cautious because they have more risks, and you may have a team that's really um an early adopter because they're what they're doing and their risks might be uh lower.
[4:55] Carolyn Woodard: So
[4:55] Carolyn Woodard: George, can you tell us the results?
[5:00] George Danilovics: The results.
[5:01] George Danilovics: Uh so not actually, there is one on here that's surprising.
[5:06] George Danilovics: So not surprising is uh the leaders uh of this poll of using AI with uh we've had IT at 45%, marketing right behind at 35.
[5:19] George Danilovics: Um, finance was the number three.
[5:23] George Danilovics: Um, so I'm curious about finance.
[5:25] George Danilovics: Um
[5:26] George Danilovics: Also not surprising to see fundraising and programming coming in at a tie.
[5:32] George Danilovics: I am surprised to see membership pretty low, or maybe that's just the nature of not of a lot of membership organizations on this call.
[5:42] George Danilovics: Um,
[5:42] George Danilovics: And then also customer service came in pretty low.
[5:46] George Danilovics: Um, only one person out of the 40 said customer service.
[5:49] George Danilovics: And I think that's probably due to you know, customer service one being customer facing.
[5:57] George Danilovics: And a lot of organizations are still hesitant about letting AI speak on behalf of them.
[6:04] George Danilovics: Um, so I'm not too surprised seeing customer service still um as low as it is in the nonprofit sector.
[6:14] Carolyn Woodard: This is so interesting.
[6:15] Carolyn Woodard: Thank you, everyone who filled out the poll.
[6:18] Carolyn Woodard: Just thank you, everyone, for doing that, and people who are putting in the chat what they're using AI for in their team.
[6:24] Carolyn Woodard: So thanks for sharing that with us.
[6:26] George Danilovics: So, our slide, you know, our poll showed a pretty good spread.
[6:29] George Danilovics: And as enough teams reach systemic, that infrastructure becomes shared.
[6:34] George Danilovics: You've got prompt libraries that people are referencing, and and new hires, when they join the organization, they learn that AI is part of the organization by default.
[6:44] George Danilovics: Um, they they get AI training and AI expectations right after they get their employee handbook.
[6:50] George Danilovics: Um,
[6:51] George Danilovics: But my favorite tell of when you get that critical mask is linguistic.
[6:55] George Danilovics: And you've heard Mimi and I talk about this previously.
[6:59] George Danilovics: Listen to how people talk.
[7:01] George Danilovics: In the early stages, everybody announces, I used AI to draft this, I used Claude to look into this research.
[7:09] George Danilovics: People stop talking about AI.
[7:11] George Danilovics: Um, when's the last time you heard somebody say, I used Outlook to send this email?
[7:17] George Danilovics: Um, I actually don't think I've ever said that in my career.
[7:21] George Danilovics: Um, so when AI stops being remarkable in that literal sense of people not remarking about it, that's when you've matured from that initiative to infrastructure.
[7:33] George Danilovics: That's the quiet boring everywhere that we've been talking about.
[7:37] Mimi Yeh: And I think along those lines, you know, if AI is a required business skill in your organization, and I think the polls have proven that it if it is not, it's quickly going to be.
[7:49] Mimi Yeh: Then developing that skill is a leadership responsibility that sits across the organization.
[7:56] Mimi Yeh: We really can't hold our staff accountable for building skills when we didn't create a deliberate plan to build it with them and in them.
[8:05] Mimi Yeh: So we want training to be more than a brown bag, a lunch and learn, a webinar like this, which is still great, but um, might need to be a more robust training program.
[8:17] Mimi Yeh: Things like role-based training that actually tie to workflows and goals and challenges that organizations face today.
[8:27] Mimi Yeh: So we would want to think about use cases and scenarios so that the finance team learns about applying AI for finance work.
[8:36] Mimi Yeh: It's also around making sure that there's protected time on people's schedules to practice using AI.
[8:45] Mimi Yeh: It's about maintaining a prompt library, which you may recognize previously was a marker of the systematic stage.
[8:53] Mimi Yeh: And a big part of it, and I think this was one of the questions or comments that was in here, is about regularly revisiting and refreshing, because as the tools change, and they do change constantly, that requires the skill set to change, and it requires our perspective and mindset about AI to change.
[9:15] George Danilovics: And, you know, here's the comparison that I'll draw for folks is security awareness training.
[9:23] George Danilovics: And I hope it's something that you're all already doing, and nobody treats it as optional, um, and nobody treats it as a one and done.
[9:31] George Danilovics: It's a structured training, it's a recurring training.
[9:34] George Danilovics: It might even be roles-based in your organization.
[9:37] George Danilovics: And we accepted that a long time ago, that that's the way it has to be.
[9:42] George Danilovics: AI competency deserves that same attention and architecture.
[9:47] George Danilovics: So, what happens next?
[9:48] George Danilovics: Systemic, as we're moving through that systemic phase.
[9:52] George Danilovics: Pilots have end dates, operations don't.
[9:55] George Danilovics: A pilot can succeed and still strand you because nobody planned for the day after the pilot.
[10:01] George Danilovics: Ownership moves from those early champions to managers.
[10:06] George Danilovics: Your champion got you here, and you need to acknowledge and thank them.
[10:10] George Danilovics: But one enthusiastic person can't scale.
[10:13] George Danilovics: The durable version of this is managers embedding AI into how their teams work.
[10:20] George Danilovics: And then the last is the scorecard changes.
[10:23] George Danilovics: Success stops being measured in the number of tools tried, a number of pilots that we started, and we begin to look at how we're measuring productivity.
[10:34] George Danilovics: If you take one thing from this slide, think of this.
[10:38] George Danilovics: For each AI effort in your organization, can you name the manager who owns it after that pilot ends?
[10:46] George Danilovics: If your answer is that person who was very excited that ran the pilot, you're still in that early experimental stage.
[10:54] Mimi Yeh: Yeah, that's true.
[10:57] Mimi Yeh: So we want to help you continue with your AI maturity journey.
[11:02] Mimi Yeh: And we want to make this actionable before we go to your questions.
[11:05] Mimi Yeh: And you see that this slide is very small, it's very simple, and it's that way deliberately.
[11:12] Mimi Yeh: That's when the programs will stall.
[11:22] Mimi Yeh: Narrower, achievable goals can be more easily accomplished, and they can also create the momentum to move forward.
[11:32] Mimi Yeh: Organizations don't change overnight.
[11:34] Mimi Yeh: The ones that make the most progress are usually taking those incremental, deliberate steps.
[11:41] Mimi Yeh: So you can see this week, we recommend that you do just two things.
[11:45] Mimi Yeh: Map your teams against those five stages that we showed earlier.
[11:49] Mimi Yeh: And remember, we're talking about your team's plural, not your one organization.
[11:55] Mimi Yeh: So you want the map.
[11:57] Mimi Yeh: It won't take you a long period of time.
[12:00] Mimi Yeh: And it'll probably give you more insight and more perspective on what to do next than most assessments would out there.
[12:10] Mimi Yeh: And then sometime this quarter, two more things to do.
[12:14] Mimi Yeh: Pick one team and decide the steps that you would want to take to move that team from its current stage to the next stage.
[12:22] Mimi Yeh: And that's it.
[12:23] Mimi Yeh: Just one team at a time, one stage over 90 days.
[12:28] Mimi Yeh: And maybe while you're at it, a little stretch goal for people, maybe replace one brown bag with one role-based training session that is tied to a team's actual work.
[12:42] Mimi Yeh: When you do that slowly and incrementally, you can repeat that play a few more times, and then you'll be a different organization within a year.
[12:51] Mimi Yeh: So this is really about changing, learning, and building gradually in order to create capability within your organization.
[13:00] Mimi Yeh: And it's a capability that will compound over time.
[13:04] Carolyn Woodard: I love that framing of it for sure, because I think to a lot of people, AI is like this giant tidal wave that's washing over all of us.
[13:12] Carolyn Woodard: So finding these small ways to move that pilot or that team into more, you know, regular, quiet, boring, and everywhere, I think that's a great way to think about it.
[13:23] Carolyn Woodard: Um,
[13:24] Carolyn Woodard: So here's our contact information, but I'm gonna move on to this slide, which has the contact information for uh Mimi and George.
[13:33] Carolyn Woodard: So you can follow them on LinkedIn and download the AI journey guide and supplemental resources at this QR code.
[13:41] Carolyn Woodard: Um and then we did have we had some questions from registration, but
[13:45] Carolyn Woodard: I want to go back to a really interesting question I found in chat that we didn't ask at the time.
[13:50] Carolyn Woodard: But um, someone said in the chat that it's hard to say what it would look like if AI was as unremarkable as email, because right now the AI tools don't seem as stable as email.
[14:03] Carolyn Woodard: So the vendors could go out of business, the tools could change dramatically.
[14:07] Carolyn Woodard: I feel like every time I open Claude, there's another update.
[14:09] Carolyn Woodard: So it's hard to consider integrating AI tools when the tool itself might not exist in a year because we're in this bubble and there's lots of market consolidation going on.
[14:21] Carolyn Woodard: So, how do you plan for that sort of thing when you're thinking about this bigger picture of really integrating AI into workflows, the way that Outlook is or the way that spreadsheets are?
[14:33] George Danilovics: I'll take uh the first step at this and then I'll let Mimi chime in.
[14:37] George Danilovics: I think when you're looking at tools, you know, you're right.
[14:41] George Danilovics: Everybody is talking about AI, and there's a new vendor and a new tool every day.
[14:46] George Danilovics: Um, so like any technology initiative, you need to do a little due diligence.
[14:52] George Danilovics: Um, you need to look at the platform, look at the vendor, and see if that's somebody that you want to be doing business with.
[14:59] George Danilovics: Um, and you you've got to take that risk and start to engage with that vendor and to see how their product fits into your organization and the work that you do.
[15:11] George Danilovics: Um,
[15:12] George Danilovics: You know any vendor you work with could go out of business.
[15:16] George Danilovics: It's a risk that we deal with on a regular basis.
[15:19] George Danilovics: But thinking about what it looks like, um, it's actually something that you won't know it or you won't see it until after it happens.
[15:30] George Danilovics: Um, and
[15:30] George Danilovics: And here's uh here's a personal example, and I'm even going to draw on uh a comment here that Greg had in the chat.
[15:37] George Danilovics: Is you know, my AI journey probably began about two years ago, and I used um uh ChatGPT to create pictures of pandas because I couldn't think of what do I use AI for.
[15:48] George Danilovics: So I just had to create pictures of you know pandas on an airplane, pandas playing baseball.
[15:53] George Danilovics: And I was like, wow, this is really cool.
[15:54] George Danilovics: AI can create pictures, that's great.
[15:57] George Danilovics: Um
[15:59] George Danilovics: But now I go to Claude for everything.
[16:03] George Danilovics: Um, if I've got a question about a technology, I ask Claude.
[16:07] George Danilovics: If I want to know a recipe, I ask Claude.
[16:10] George Danilovics: If I'm curious about how to build something, I ask Claude.
[16:15] George Danilovics: Whereas before I used to go to Google, I used to go to a search engine, I used to go to a vendor's page.
[16:21] George Danilovics: So that just happened over time gradually, and I never noticed a behavior change until after it happened.
[16:31] George Danilovics: You know, where I used to go to the Google search button on my phone, I now click the Claude app.
[16:37] George Danilovics: Um,
[16:38] George Danilovics: So that systemic piece, it's hard to see it as it's happening.
[16:44] George Danilovics: When you look back, you'll realize when it happened.
[16:47] Mimi Yeh: Um, I'll add a little bit onto this, and then I know we've got so many more questions and comments to parse through.
[16:55] Mimi Yeh: I think it's absolutely true that tools change.
[16:58] Mimi Yeh: I am old enough to remember when Yahoo and Ask Jeeves and all these other engines and methods of getting information, um, we used tools that do not exist anymore today.
[17:14] Mimi Yeh: Or if they do, they're probably all corralled up in Bending Spoon or one of those other companies that have smooshed together all of those cats and dogs.
[17:24] Mimi Yeh: So the tools change, but why you're using the tool and why your organization chose to apply AI to achieve a particular part of your strategy, your mission, or your goals, that didn't really change.
[17:40] Mimi Yeh: So I guess one thing I would say is start with your strategy.
[17:45] Mimi Yeh: Start with what is the opportunity or the challenge that you want AI to solve or assist you with.
[17:53] Mimi Yeh: Um, and on those same points, maybe a side tip is to document that journey.
[18:01] Mimi Yeh: Because if it migrates from one AI tool to the other, the logic and the thought process that you used probably will not change.
[18:11] Mimi Yeh: That will stay sound and having that document might make it easier to feed those prompts or to feed that, create that Gem that is going to be customized to your organization with the proper prompts and the way that you want that AI persona to uh behave when it's interacting with your staff.
[18:33] Mimi Yeh: Um so you know, document that journey and be clear about why you made the choices that you made.
[18:40] Carolyn Woodard: Those are so such great pieces of advice, Mimi.
[18:43] Carolyn Woodard: Thank you so much.
[18:44] Carolyn Woodard: Um,
[18:45] Carolyn Woodard: We're getting close to the end.
[18:46] Carolyn Woodard: I'm gonna open a huge can of worms because we are close to the end.
[18:51] Carolyn Woodard: Um, Greg asked this really brilliant question in the QA that I saved to the end because I knew we like this could be an hour-long discussion, basically.
[19:01] Carolyn Woodard: Um,
[19:02] Carolyn Woodard: But I wondered if, kind of as closing thoughts, uh, we've started in this webinar asking about like maturity.
[19:08] Carolyn Woodard: So you're already using AI.
[19:10] Carolyn Woodard: How do you get to that next phase where it's kind of really integrated?
[19:14] Carolyn Woodard: And Greg asks, how do you address and demonstrate care for genuine concerns about ethics, social impact, all of the issues that we know about AI and that nonprofits care about around AI while you're trying to put this formalized and mature adoption in place?
[19:32] Carolyn Woodard: So he says, many of the people in my sector are opposed to AI based on economic and ethical concerns.
[19:39] Carolyn Woodard: I don't want to ignore that.
[19:40] Carolyn Woodard: And I want to ensure that we move forward at a pace that is good for our community and, of course, for your organization and then for yourself as well, like what you feel comfortable with.
[19:49] Carolyn Woodard: So when you run into that, these sentiments at clients and when you're working on AI and with your colleagues, do you have maybe some prompts for us of ways to think about it?
[20:01] Carolyn Woodard: Since this is a very long conversation, we only have a few minutes, but how do you start answering that question?
[20:07] Carolyn Woodard: I ask you a really, really easy one.
[20:09] George Danilovics: Easy question, yeah.
[20:11] George Danilovics: And I've heard this, you know, this concern, you know, from from multiple individuals and organizations as it pertains to AI, both on the social piece, the the concerns over uh energy and what it's doing to our environment.
[20:27] George Danilovics: Um and
[20:28] George Danilovics: I think all of those are valid concerns.
[20:31] George Danilovics: I think it is something that we as users and stewards of AI should be looking at other organizations that are trying to make sure AI is used as a uh a force for good.
[20:47] George Danilovics: Um and that, you know, as it's using resources, we use it in a productive manner.
[20:54] George Danilovics: And it's not used to the detriment of society.
[20:57] George Danilovics: Um
[20:58] George Danilovics: I don't think any one individual or one group can sway an entire technology.
[21:06] George Danilovics: Um, but each organization does have to think about how they are best going to use a technology or not.
[21:13] George Danilovics: Um, and some of those conversations may require, you know, consulting with your board of directors, um, particularly around, you know, if there's there's concerns on the social piece or energy or resources piece or the ethical side of you know, where an organization has said, you know, where are those red lines of where we're not going to use AI.
[21:35] George Danilovics: Um and
[21:37] George Danilovics: To have that as a board decision, not just a staff decision.
[21:41] George Danilovics: Um, and so I think it is something we have to look at holistically and we we have to be cognizant of some of the the trade-offs and some of the consequences of the technology.
[21:55] Mimi Yeh: Yeah, I think that's right.
[21:57] Mimi Yeh: And I I would say, you know, I think we can all agree that um AI every major technology raises new ethical questions and dilemmas with it.
[22:11] Mimi Yeh: AI is not different in that case.
[22:13] Mimi Yeh: It may be different in what the ethical questions are and the degree of the concern, but you know.
[22:22] Mimi Yeh: I think a lot of this what I think about is the answer to whether and how to use AI should be reflective of your overall technology strategy and your technology approach because there's lots of other considerations and concerns and ethical dilemmas with many other technical tools that are out there.
[22:46] Mimi Yeh: There's all kinds of concerns associated with flying and using a plane instead of other modes of transportation.
[22:57] Mimi Yeh: There's ethical concerns associated with using tools like Salesforce because of the way that their content is being used by certain actors and players out there.
[23:08] Mimi Yeh: So we may not be able to eliminate all of the risk.
[23:12] Mimi Yeh: We might have to instead manage it thoughtfully.
[23:15] Mimi Yeh: And to what George said, be transparent about how you choose to use it and make sure that the use of it still aligns back with your mission and your values.
[23:28] Mimi Yeh: And you're still operating in a way that you're protecting sensitive data and respecting people's privacy and being cognizant of potential bias that might exist within different AI tools and how you as an organization elect to proactively manage those.
[23:49] Carolyn Woodard: I love both of those answers.
[23:51] Carolyn Woodard: Thank you for weighing in with on something that is such a serious topic.
[23:56] Carolyn Woodard: That really helped.
[23:57] Carolyn Woodard: I think I just want to echo what you said about transparency.
[24:00] Carolyn Woodard: We've been really recommending that it is an all-staff ongoing conversation, like also with your executive team, also with your board, because you can find that you have staff that like you have a policy, but they're not okay with it.
[24:14] Carolyn Woodard: So you really need to be constantly having this evolving conversation about your values and how this relates to what your values are.
[24:23] Carolyn Woodard: And another thing I just wanted to say that's I've been thinking about recently is especially around like environmental issues, looking to like the big environmental organizations and what they're doing.
[24:34] Carolyn Woodard: And many of them have put out like how to think about AI, how to use AI, and you know, kind of where they're coming to it from.
[24:41] Carolyn Woodard: They have a lot more experience than I do in terms of what it's doing to the environment.
[24:45] Carolyn Woodard: So kind of hooking on to people in our peer group that have more experience than we do is another way to address those fears and concerns.
[24:53] Carolyn Woodard: Um,
[24:54] Carolyn Woodard: So I want to move on.
[24:55] Carolyn Woodard: Uh, we only have a minute or two left.
[24:57] Carolyn Woodard: So I wanted to, I think we hit the learning objectives really well.
[25:00] Carolyn Woodard: Thank you both, George and Mimi.
[25:02] Carolyn Woodard: Uh, we wanted to talk about it, the AI maturity model, and give you some clues of uh how to figure out where your nonprofit fits in there.
[25:11] Carolyn Woodard: Um, how do you build AI as a standard core competency across your workforce?
[25:15] Carolyn Woodard: We talked about the training and making it boring and everywhere.
[25:19] Carolyn Woodard: Um, and then what does strategic AI implementation look like and some of these best practices around, you know, training, um, measuring, thinking about this maturity model.
[25:31] Carolyn Woodard: And I love the idea that you don't have to get to the top of it, but just figuring out where you are on that model will help you with where you want to get to as well.
[25:40] Carolyn Woodard: So um I want to tell you all about the webinar that we have next month.
[25:44] Carolyn Woodard: Uh, if we invite you back for that, I'm gonna be leading that presentation with my colleague Eric Solce, who uh is helping me on a project that I'm doing right now.
[25:55] Carolyn Woodard: So we're gonna report back on how it's going.
[25:57] Carolyn Woodard: Uh,
[25:58] Carolyn Woodard: I have an ongoing project using AI to help build a data lake for marketing.
[26:03] Carolyn Woodard: And we're gonna talk about the structure that we've come up with that helped me manage this giant project with a little tech coaching, a lot of AI.
[26:13] Carolyn Woodard: We're gonna share how the project evolved, what didn't go as well as we hoped, what we learned.
[26:18] Carolyn Woodard: Uh, we think that kind of a structure has a lot of potential for nonprofit AI projects.
[26:23] Carolyn Woodard: So if you have been putting something off because you don't have the technical know-how to do it, or it's just this giant thing that you don't have time for, I know some of us put in the chat, like, what would you do if you could do it?
[26:35] Carolyn Woodard: Um, so if you're thinking about that, um it uh whether it's you know building a data lake for some other purpose or analyzing years of program reports or you know, whatever it is that you've just been putting off and want to get started on, that's what we're gonna talk about next month.
[26:49] Carolyn Woodard: Um,
[26:50] Carolyn Woodard: That is at 3 p.m.
[26:51] Carolyn Woodard: Eastern Noon Pacific on Wednesday, August 19th.
[26:55] Carolyn Woodard: I'm gonna share that link with you in the chat.
[26:58] Carolyn Woodard: Uh, it's gonna be on our website, community it.com.
[27:02] Carolyn Woodard: And then please join us on Reddit at r/ nonprofit IT management for another 30 minutes or so.
[27:10] Carolyn Woodard: We'll be uh answering your questions that we didn't get to that were here in chat.
[27:15] Carolyn Woodard: Um, and oh yes, I'm sorry, I put I put the specific uh link in that goes exactly to the QA, but you can also find us on r slash nonprofit IT management.
[27:27] Carolyn Woodard: And um,
[27:28] Carolyn Woodard: Thank you so, so much for joining us, everyone.
[27:31] Carolyn Woodard: And an hour of your time is a gift, and we really appreciate it.
[27:34] Carolyn Woodard: Thank you for spending this afternoon time with us, George and Mimi.
[27:38] Carolyn Woodard: I just appreciate your time as well and all the thought and effort that went into this presentation.
[27:44] Carolyn Woodard: It really helped me.
[27:45] Carolyn Woodard: I change a little bit, reframe how I'm thinking about this also.
[27:49] Carolyn Woodard: Um, so thank you again so much for coming.
[27:52] George Danilovics: Thank you.
[27:53] Mimi Yeh: Thank you all.
[27:54] Mimi Yeh: Have a great day.
[27:55] Mimi Yeh: Thanks.
[27:57] Community IT Intro: Thank you for joining this Community IT podcast, part two.
[28:01] Community IT Intro: You can find part one in your podcast feed if you subscribe wherever you listen to podcasts.

Shows are listed from Apple Podcasts' charts and directory, and Podcast Index where it is used. Every show and episode belongs to its publisher and plays from the publisher's own feed; this site hosts none of them.